Life Cycle Inventory (LCI)

The Life Cycle Inventory (LCI) is the second phase of a Life Cycle Assessment (LCA) under ISO 14044. It consists of collecting and quantifying all input and output flows (materials, energy, water, emissions and waste) associated with a product, process or service within the defined system boundaries. The LCI provides the numerical foundation later used to calculate environmental impacts in the Life Cycle Impact Assessment (LCIA) phase.

Purpose of the LCI

• Provide a detailed, transparent inventory of the resources used and emissions generated.
• Help identify high-impact hotspots to guide eco-design improvements.
• Serve as a verifiable input for Environmental Product Declarations (EPDs) and Digital Product Passports (DPPs).
• Improve comparability between alternatives when consistent boundary and allocation rules are applied.

Types of data in an LCI

• Material inputs: minerals, biomass, chemicals and fuels.
• Energy inputs: electricity, heat, steam and auxiliary fuels.
• Water inputs: blue, green or recycled water withdrawals.
• Outputs: air emissions (CO2, CH4, NOx), water discharges (COD, metals), solid waste (hazardous or non-hazardous) and co-products.
• Internal transport: distances and modes between unit processes.
• Capital inventory (optional): machinery, infrastructure and amortised buildings.

Step-by-step methodology

1. Define the system boundaries and the functional unit.
2. Break the system down into unit processes (a process map).
3. Collect primary data from facilities and direct suppliers (Tier 1).
4. Assign secondary data from databases such as ecoinvent, Agri-footprint or GaBi for background processes.
5. Balance inputs and outputs, checking mass and energy conservation.
6. Normalise and scale the results to the functional unit.
7. Document data quality: precision and temporal, geographic and technological representativeness.
8. Carry out an independent expert critical review (required for Type III EPDs or comparative studies).

Data sources and tools

• Databases: ecoinvent, GaBi, USLCI, ELCD, IDEA and Oekobau.dat.
• Software: SimaPro, OpenLCA, GaBi, One Click LCA, Tally and Brightway2.
• On-site measurement: flow meters, IoT sensors and SCADA data for energy and water.
• Suppliers: safety data sheets, sustainability reports and tailored questionnaires.

Common challenges

• Lack of primary data at Tier 2 and Tier 3 of the supply chain.
• Co-product allocation: multi-output systems require mass, energy or economic-value rules.
• Confidentiality: suppliers may be reluctant to share information.
• Temporal inconsistencies: datasets from different years reduce accuracy.
• Change over time: technologies and electricity mixes evolve and require periodic updates.

Best practices

• Set up supplier data agreements for annual reporting of inputs.
• Use plant digital twins for automated, granular data capture.
• Follow ILCD recommendations for documenting data quality.
• Apply Monte Carlo sensitivity analysis to assess uncertainty.
• Align the LCI with water footprint and embodied-carbon assessments for multidimensional consistency.

Illustrative example: low-carbon clinker

For a functional unit of one tonne of clinker, a simplified inventory would record inputs such as limestone, marl, electricity and a fuel like petroleum coke, and outputs dominated by process CO2 from calcination plus combustion emissions, minor NOx and particulates, and granulated slag as a co-product. Replacing part of the fossil fuel with biomass residues lowers the combustion-related CO2 recorded in the inventory. The exact figures depend on the plant and the data sources used, which is why transparent documentation of factors and boundaries is essential.

Integration with LCIA and EPDs

The LCI feeds the LCIA phase using methods such as CML, ReCiPe 2016, AWARE and TRACI. Results are then summarised in verified EPDs under EN 15804+A2 or ISO 21930, and the relevant LCI data can also be linked to the Digital Product Passport required by the ESPR Regulation.

Future outlook

• Automation through IoT and digital traceability for near-real-time data.
• Sector-wide harmonisation of factors and nomenclatures.
• Use of machine learning to fill gaps and improve data quality.
• Dynamic LCA approaches that capture temporal variability in processes and electricity mixes.

A comprehensive, high-quality LCI is the cornerstone of any data-driven sustainability strategy. It provides the transparency needed to eco-design, reduce carbon and water footprints, and comply with emerging European requirements such as the ESPR and the CSRD. At Manglai we help companies measure their environmental footprint and prepare their sustainability reporting. Discover how Manglai can help you.